High-level synthesis (HLS) is a key automation technique for digital circuit design.However, the need for expertise and time in pragma tuning remains challenging for HLS.To address this, a novel approach called Deep Inverse Design for HLS (DID4HLS) is proposed.DID4HLS integrates graph neural networks and generative models to optimize hardware designs for compute-intensive algorithms.